About the role

Machine Learning Engineer intern at Bree developing and deploying ML models for critical FinTech applications. Designing and architecting ML pipelines to enhance financial services.

Responsibilities

Design, train, and deploy scalable machine learning models for critical FinTech applications, including credit risk assessment, fraud detection, and personalized financial recommendations, using frameworks like PyTorch and LightGBM.
Architect ML pipelines integrating with backend systems to process high-throughput data streams with low-latency inference for real-time decision-making.
Leverage AI tools to automate experimentation, hyperparameter tuning, and test-driven ML development, accelerating the delivery of robust, production-ready models.
Support the full ML lifecycle, including feature engineering, model evaluation, A/B testing, monitoring for drift, and seamless scaling to support explosive user growth while ensuring compliance with financial regulations.
Experiment with advanced techniques in deep learning and reinforcement learning to push the boundaries of what's possible in consumer finance.

Professional experience in building and deploying production ML systems and handling imbalanced datasets in high-stakes domains like finance or e-commerce.
Good understanding of traditional ML systems and modern deep learning/reinforcement learning architectures, with a track record of applying them to real-world problems.
Competitive ML experience (e.g., top rankings in Kaggle, NeurIPS challenges, or open-source contributions) is a bonus, demonstrating your ability to innovate under constraints and deliver high-performance models.
Architectural thinking to solve ambiguous, data-driven problems in fast-paced settings, with experience scaling ML systems under explosive growth while maintaining accuracy, fairness, and explainability.
Exceptional collaboration and communication skills, including the ability to explain complex ML concepts to non-technical stakeholders, thriving in low-churn teams focused on excellence, ethical AI, and long-term impact.